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Gene Regulatory Network Analysis Identifies Sex-Linked Differences in Colon Cancer Drug Metabolism

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Gene Regulatory Network Analysis Identifies Sex-Linked Differences in Colon Cancer Drug Metabolism Cancer Genome and Epigenome Research Gene Regulatory Network Analysis Identifies Sex-Linked Differences in Colon Cancer Drug Metabolism Camila M. Lopes-Ramos1,2, Marieke L. Kuijjer1,2, Shuji Ogino3,4,5,6, Charles S. Fuchs7,8,9, Dawn L. DeMeo10,11, Kimberly Glass10, and John Quackenbush1,2,12 Abstract Understanding sex differences in colon cancer is essential females with greater targeting showed an increase in 10-year to advance disease prevention, diagnosis, and treatment. overall survival probability, 89% [95% confidence interval Males have a higher risk of developing colon cancer and a (CI), 78–100] survival compared with 61% (95% CI, 45–82) lower survival rate than women. However, the molecular for women with lower targeting, respectively (P ¼ 0.034). features that drive these sex differences are poorly under- Our network analysis uncovers patterns of transcriptional stood. In this study, we use both transcript-based and gene regulation that differentiate male and female colon cancer regulatory network methods to analyze RNA-seq data from and identifies differences in regulatory processes involving The Cancer Genome Atlas for 445 patients with colon cancer. the drug metabolism pathway associated with survival in We compared gene expression between tumors in men and women who receive adjuvant chemotherapy. This approach women and observed significant sex differences in sex chro- canbeusedtoinvestigatethemolecularfeaturesthatdrive mosome genes only. We then inferred patient-specificgene sex differences in other cancers and complex diseases. regulatory networks and found significant regulatory differ- ences between males and females, with drug and xenobiotics Significance: A network-based approach reveals that sex- metabolism via cytochrome P450 pathways more strongly specific patterns of gene targeting by transcriptional regulators targeted in females. This finding was validated in a dataset of are associated with survival outcome in colon cancer. This 1,193 patients from five independent studies. While target- approach can be used to understand how sex influences ing, the drug metabolism pathway did not change overall progression and response to therapies in other cancers. survival for males treated with adjuvant chemotherapy, Cancer Res; 78(19); 5538–47. Ó2018 AACR. Introduction head and neck, esophagus, lung, and liver, males have a higher risk and higher mortality rates than females (1). Even though the Significant differences between the sexes are observed during higher risk in males might be attributed partially to occupational the development and progression of diseases, influencing disease exposures and/or behavioral factors, such as diet, smoking, and incidence and survival. For many cancer types, such as colon, skin, alcohol consumption, after adjusting for these risk factors males still have a higher cancer risk, although residual confounding cannot be excluded (1–3). In colon cancer, females not only 1Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, Massachusetts. 2Department of Biostatistics, Harvard T.H. have reduced risk relative to males, but also have a better prog- Chan School of Public Health, Boston, Massachusetts. 3Department of Epide- nosis (4–6). Furthermore, females have a higher survival benefit miology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts. from 5-fluorouracil (5-FU)–based adjuvant chemotherapy as 4Program in MPE Molecular Pathological Epidemiology, Department of Pathol- compared with males (7). Pharmacokinetics also vary between ogy, Brigham and Women's Hospital and Harvard Medical School, Boston, 5 the sexes; females experience greater toxicity from certain che- Massachusetts. Department of Oncologic Pathology, Dana-Farber Cancer motherapies, including 5-FU, consistent with the lower 5-FU Institute, Boston, Massachusetts. 6Broad Institute of MIT and Harvard, Cam- bridge, Massachusetts. 7Yale Cancer Center, New Haven, Connecticut. 8Depart- clearance observed in females (8, 9). ment of Medicine, Yale School of Medicine, New Haven, Connecticut. 9Smilow Sex differences in colon cancer have been largely attributed to Cancer Hospital, New Haven, Connecticut. 10Channing Division of Network sex hormones, yet the molecular mechanisms have not been Medicine, Brigham and Women's Hospital, and Harvard Medical School, Boston, established and clinical studies are contradictory (9). In gen- Massachusetts. 11Division of Pulmonary and Critical Care Medicine, Brigham and 12 eral, studies point to the protective role of female hormones Women's Hospital, Boston, Massachusetts. Department of Cancer Biology, (estrogen) during colon cancer development and to the Dana-Farber Cancer Institute, Boston, Massachusetts. increased risk associated with male hormones (testosterone; Note: Supplementary data for this article are available at Cancer Research refs.10–12). The circadian system might also explain the better Online (http://cancerres.aacrjournals.org/). prognosis in females, polymorphisms in the CLOCK sequence, Corresponding Author: John Quackenbush, Harvard T.H. Chan School of Public and the expression levels of miRNAs regulating the clock-genes Health and Dana-Farber Cancer Institute, 655 Huntington Ave, Boston, MA 02115. were associated with longer overall survival of females with Phone: 617-432-9028; Fax: 617-432-5619; E-mail: [email protected] metastatic colorectal cancer compared with males (13). doi: 10.1158/0008-5472.CAN-18-0454 Although previous studies focused on a few targeted genes, a Ó2018 American Association for Cancer Research. systems-based analysis that integrates multi-omics data can 5538 Cancer Res; 78(19) October 1, 2018 Downloaded from cancerres.aacrjournals.org on September 29, 2021. © 2018 American Association for Cancer Research. Regulatory Networks Identify Sex Differences in Colon Cancer provide insights into sex-specific regulatory processes associat- ples, removed samples that were not annotated for sex, and used ed with clinical outcome. principal component analysis (using the plotOrd function in Regulatory networks characterize the complex cellular process- metagenomeSeq 1.12.1; ref. 20) on genes located on the Y es defined by a combination of signaling pathways and cell-type chromosome to identify and remove 9 potential sex-misanno- specific regulators. Each phenotype is defined by a characteristic tated samples. After performing these quality control steps, the network, while differences in network structures can shed light discovery dataset included 445 primary colon tumor samples upon the biological processes that distinguish phenotypes. For before treatment, from 238 males and 207 females. example, gene regulatory network analysis has uncovered regu- We filtered lowly expressed genes by removing genes with less latory differences between cell lines and their tissues of origin, and than one count per million (CPM) in at least 104 samples, using between cancer subtypes (14, 15). Network-modeling approaches the cpm function from R package edge R 3.18.1 (21), which have also been valuable in determining sex-specific regulatory corresponded to 5,571 of 20,249 genes. We chose 104 samples features in healthy tissues and in disease (16, 17). because that represents half of the samples in the smaller sub- Although both the risk for and outcome of colon cancer are group. To retain the same set of genes in the discovery and different between men and women, clinical management is sex validation datasets, and the same set of genes for the differential independent. This may be because the molecular features that expression, and differential targeting analysis, we kept only the drive these sex differences are poorly understood. We used net- genes overlapping the filtered genes in the discovery dataset, the work-modeling approaches, PANDA (18) and LIONESS (18, 19), validation dataset, and the genes in the TF/target gene regulatory to infer colon cancer patient–specific gene regulatory net- prior used for creating the gene regulatory networks (see sections: works. We compared the male and female networks to identify "Validation dataset" and "Single-sample gene regulatory net- genes that were targeted by transcription factors (TF) in a sex- works and differential targeting analysis"). This corresponded to specific manner (Fig. 1). We found that genes involved in drug and 12,817 genes, which included genes on the sex chromosomes. xenobiotics metabolism via cytochrome P450 were more strongly The expression data generated by TCGA were normalized using targeted by regulatory TFs in females; these results were validated smooth quantile normalization (22), and batch was corrected for in an independent dataset. Moreover, greater regulatory targeting sequencing platforms (IlluminaGA and IlluminaHiSeq) and ship- of the drug metabolism pathway was predictive of longer survival ment date, as implemented in the R package qsmooth available in women who received adjuvant chemotherapy, but not in men. on Github (https://github.com/stephaniehicks/qsmooth). Validation dataset Materials and Methods We searched the Gene Expression Omnibus (GEO) repository Discovery dataset for colon cancer studies that included patient survival data and We downloaded level 3 RNASeqV2 and clinical data for colon were obtained from the same microarray platform (Affymetrix cancer from The Cancer Genome Atlas (TCGA) on June 16, 2016 Human Genome U133 Plus 2.0 Array). The validation dataset (https://tcga-data.nci.nih.gov). We kept only primary tumor sam- contained five independent
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